Regarding people who play chess against computers, some players like playing only bots because of the psychological pressure that comes from playing against human players. You don’t get as upset about a loss if it’s just to a machine. I think that would count for a significant fraction of those players.
Martin Fell
Karma: 88
There are also some new glitch tokens for GPT-3.5 / GPT-4, my favourite is ” ForCanBeConverted”, although I don’t think the behaviour they produce is as interesting and varied as the GPT-3 glitch tokens. It generally seems to process the token as if it was a specific word that varies depending on the context. For example, with ” ForCanBeConverted”, if you try asking for stories, you tend to get a fairly formulaic story but with the randomized word inserted into it (e.g. “impossible”, “innovate”, “imaginate”, etc.). I think that might be due to the RLHF harming the model’s creativity though, biasing it towards “inoffensive” stories, which would make access to the base model more appealing.
Also, another thought that comes to mind—is it possible that the unexplained changes to the GPT-3 model’s output could be related to changes in the underlying hardware or implementation, rather than further training? I’m only thinking this because of the nondeterministic behaviour you get at 0 temperature (especially in the case of glitch tokens where floating-point rounding could make a big difference in the top logits).
It’s really a shame that they aren’t continuing to make GPT-3 available for further research, and I really hope they reconsider this. Your deep dives into the mystery and psychology behind these tokens has been fascinating to read.
This fits with my experience talking to people unfamiliar with the field. Many do seem to think it’s closer to GOFAI, explicitly programmed, maybe with a big database of stuff scraped from the internet that gets mixed-and-matched depending on the situation.
Examples include:
Discussions around the affect of AI in the art world often seem to imply that these AIs are taking images directly from the internet and somehow “merging” them together, using a clever (and completely unspecified) algorithm. Sometimes it’s implied or even outright stated that this is just a new way to get around copyright.
Talking about ChatGPT with some friends who have some degree of coding / engineering knowledge, they frequently say things like “it’s not really writing anything, it’s just copied from a database / the internet”.
I’ve also read many news articles and comments which refer to AIs being “programmed”, e.g. “ChatGPT is programmed to avoid violence”, “programmed to understand human language”, etc.
I think most people who have more than a very passing interest in the topic have a better understanding than that though. And I suspect that many completely non-technical people have such a vague understanstanding of what “programmed” means that it could apply to training an LLM or explictly coding an algorithm. But I do think this is a real misunderstanding that is reasonably widespread.
Sounds like a very interesting project! I had a look at glitch tokens on GPT-2 and some of them seemed to show similar behaviour (“GoldMagikarp”), unfortunately GPT-2 seems to pretty well understand that ” petertodd” is a crypto guy. I believe similar was true with ” Leilan”. Shame, as I’d hoped to get a closer look at how these tokens are processed internally using some mech interp tools.
Note that there are glitch tokens in GPT3.5 and GPT4! The tokenizer was changed to a 100k vocabulary (rather than 50k) so all of the tokens are different, but they are there. Try ” ForCanBeConverted” as an example.
If I remember correctly, “davidjl” is the only old glitch token that carries over to the new tokenizer.
Apart from that, some lists have been created and there do exist a good selection.
Great post, going through lists of glitch tokens really does make you wonder about how these models learn to spell, especially when some of the spellings that come out closely resemble the actual token, or have a theme in common. How many times did the model see this token in training? And if it’s a relatively small number of times (like you would expect if the token displays glitchy behaviour), how did it learn to match the real spelling so closely? Nice to see someone looking into this stuff more closely.
Nice idea and very well implemented. Quite enjoyable too, I hope you keep it going. Just a quick idea that came to mind—perhaps the vote suggestion could be hidden until you click to reveal it perhaps? Think I can feel a little confirmation bias potentially creeping into my answers (so I’m avoiding looking at the suggestion until I’ve formed my own opinion). Apologies if there is already an option for that or if I missed something. I mostly jumped right in after skimming the tutorial since I have tried reading neurons for meaning before.
Thanks for posting this! Coincidentally, just yesterday I was wondering if there were any mech interp challenges like these, it seems to lend itself to this kind of thing. Had been considering trying to come up with a few myself.
Yes that’s what I take would happen too unless I’m misunderstanding something? Because it would seem far more probable for *just* your consciousness to somehow still exist, defying entropy, than for the same thing to happen to an entire civilization (same argument why nearly all Boltzmann brains would be just a bare “brain”).
Hah yes there is quite a lot of weirdness associated with glitch tokens that I don’t think has been fully investigated. Some of them it seems to sort-of-know what the spelling is or what their meaning is, others it has no idea and they change every time. And the behaviour can get even more complicated if you keep using them over and over in the same conversation—some ordinary tokens can switch to behaving as glitch tokens. Actually caused me some false positives when searching for these.
The behaviour here seems very similar to what I’ve seen when getting ChatGPT to repeat glitch tokens—it runs into a wall and cuts off content instead of repeating the actual glitch token (e.g. a list of word will be suddenly cut off on the actual glitch token). Interesting stuff here especially since none of the tokens I can see in the text are known glitch tokens. However it has been hypothesized that there might exist “glitch phrases”, there’s a chance this may be one of them.
Also, I did try it in the OpenAI playground and the various gpt 3.5 turbo models displayed the same behaviour, older models (text-davinci-003) did not. Note that there was a change of the tokenizer to a 100k tokenizer on gpt-3.5-turbo (older models use a tokenizer with 50k tokens). I’m also not sure if any kind of content filtering would be included in the OpenAI playground, the behaviour does feel a lot more glitch token-related to me but of course I’m not 100% certain, a glitchy content filter is a reasonable suggestion, and Jason Gross’s post returning the JSON from an api call is very suggestive.
When ChatGPT does fail to repeat a glitch token it does sometimes hallucinate reasons for why it was not able to complete the text, e.g. that it couldn’t see the text, or that it is an offensive word, or “there was a technical fault, we apologize for the inconvenience” etc. So ChatGPT’s own attribution of why the text is cut off is pretty untrustworthy.
Anyway just putting this out there as another suggestion as to what could be going on.
Thanks, I appreciate it—I didn’t really understand the downvotes either, my beliefs don’t even seem particularly controversial (to me). Just that I think it’s really important to understand where COVID came from (and the lab leak theory should be taken seriously) and try to prevent something similar from happening in the future. I’m not much interested in blaming any particular person or group of people.
The seeming lack of widespread concern about the origins of COVID given that if it is of artificial origin it would be perhaps the worst technologically-created accidental disaster in history (unless I’m missing something) is really very disappointing.
Hah yeah I’m not exactly loaded either, it’s pretty much all colab notebooks for me (but you can get access to free GPUs through colab, in case you don’t know).
Has any tried training LLMs with some kind of “curriculum” like this? With a simple dataset that starts with basic grammar and simple concepts (like TinyStories), and gradually moves onto move advanced/abstract concepts, building on what’s been provided so far? I wonder if that could also lead to more interpretable models?
Since it seems that glitch tokens are caused by certain sequences of text appearing in the training corpus for the tokenizer much more often than they do in the LLM training data, something like that might work. But there also seem to exist “glitch phrases” or “unspeakable phrases”, i.e. sequences of tokens of extremely low probability to the model that could create some strange behaviour too, and it seems at least plausible to me that these kinds of phrases could still be generated even if countermeasures were taken to prevent glitch tokens from being created. Glitch phrases though are a bit more difficult to find without access to the model.
Thanks, appreciate the suggestion, there’s definitely a lot of room to go into more depth and I’ll definitely check that out
Thanks, I’ll rephrase that part for clarity
That actually makes a lot of sense to me—suppose that it’s equivalent to episodic / conscious memory is what is there in the context window—then it wouldn’t “remember” any of its training. These would appear to be skills that exist but without any memory of getting them. A bit similar to how you don’t remember learning how to talk.
It is what I’d expect a self-aware LLM to percieve. But of course that might be just be what it’s inferred from the training data.